Scenario-based portfolio selection of investment projects with incomplete probability and utility information

In the selection of investment projects, it is important to account for exogenous uncertainties (such as macroeconomic developments) which may impact the performance of projects. These uncertainties can be addressed by examining how the projects perform across several scenarios; but it may be difficult to assign well-founded probabilities to such scenarios, or to characterize the decision makers’ risk preferences through a uniquely defined utility function. Motivated by these considerations, we develop a portfolio selection framework which (i) uses set inclusion to capture incomplete information about scenario probabilities and utility functions, (ii) identifies all the non-dominated project portfolios in view of this information, and (iii) offers decision support for rejection and selection of projects. The proposed framework enables interactive decision support processes where the implications of additional probability and utility information or further risk constraints are shown in terms of corresponding decision recommendations.     

A decision model for interdependent information system project selection

Existing methods for information system (IS) project selection neglect an important aspect of information technology, namely the interdependencies that exist among various IS applications (projects). Recognizing and modeling these project interdependencies provides valuable cost savings and greater benefits to organizations. In this paper, an IS project selection model is developed that identifies and models benefit, resource and technical interdependencies among candidate projects. The proposed model is formulated as a nonlinear 0–1 programming problem and represents a significant addition to existing IS, capital budgeting and R&D project selection models. The model is converted, using linearization techniques, and tested (validated) by applying it to real-world IS project selection data. By comparing the performance of this model with existing project selection models, the contribution of this model is highlighted.     

模糊环境下带有平衡条件的投资项目评估与选择决策方法

文章提出在模糊环境下求解带有平衡条件的投资项目评估与选择问题的决策方法。该方法由模糊综合评价系统和项目选择的模糊整数规划模型两部分组成。其中模糊综合评价系统采用三角模糊数来描述决策人对项目的主观评价以及多个评价因素的综合，而模糊整数规划模型则描述了各种不同门类利益之间的平衡。最后以实例说明该方法的应用。     

The optimal solution of ESG portfolio selection models that are based on the average ESG score

Recently, various models have been proposed to engage portfolio selection or ESG investments. In this brief report, we solve the problem of optimal portfolio selection of arbitrary ESG utility functions where the ESG preference function is based on the average ESG score. The proposed optimal solution shows that the impact of the ESG score and the expected return vectors on the optimal weights are equal, up to a scalar, regardless of the utility function of the investors.     

Multicriteria decision making on maintenance: Spares and contracts planning

The competitiveness of an industrial system is directly related to decision making in areas of product support logistics, such as the maintenance area. Multicriteria decision making takes into account various aspects associated with competitiveness of the system. This paper presents multicriteria decision models for two maintenance problems: repair contract selection and spares provisioning. In the repair contract problem the model incorporates consequences modelled through a multiattribute utility function. These consequences consist of contract cost and system performance, represented by the system interruption time. Two criteria (risk and cost) are combined through a multiattribute utility function in the spares provisioning decision model. This paper presents the formulation and derivations for both models and the numerical application illustrates the use of models including sensitivity analysis.     

Using Multicriteria Methods in the Early Stages of New Product Development

The R&D manager is commonly faced with the problem of deciding which projects to fund to meet overall corporate and technical goals. Because outcomes can rarely be predicted with certainty, decisions aimed at striking a balance between cost and risk are likely to involve some amount of redundancy at the project level. The intent of this paper is to examine the difficulties that arise when trying to pursue a parallel strategy in the presence of multiple objectives. The basic elements of the problem include a set of projects, a set of objectives, the associated probability measures relating effort to success, budgetary and performance constraints, and a utility function defined on the range of outcomes. In the model it will be assumed that each project contributes to one or more objectives, and that the selection criterion is based on expected utility maximization. With this in mind, the problem is formulated as a probabilistic goal programme and solved with a heuristic that computes the K best funding schemes. Results are presented for a case involving the development of a non-petroleum-powered vehicle which demonstrate the robustness of the algorithm and the implications of the underlying decision rules.     

A review of credibilistic portfolio selection

This paper reviews the credibilistic portfolio selection approaches which deal with fuzzy portfolio selection problem based on credibility measure. The reason for choosing credibility measure is given. Several mathematical definitions of risk of an investment in the portfolio are introduced. Some credibilistic portfolio selection models are presented, including mean-risk model, mean-variance model, mean-semivariance model, credibility maximization model, α-return maximization model, entropy optimization model and game models. A hybrid intelligent algorithm for solving the optimization models is documented. In addition, as extensions of credibilistic portfolio selection approaches, the paper also gives a brief review of some hybrid portfolio selection models.     

High-dimensional variable screening and bias in subsequent inference,with an empirical comparison

We review variable selection and variable screening in high-dimensional linear models. Thereby, a major focus is an empirical comparison of various estimation methods with respect to true and false positive selection rates based on 128 different sparse scenarios from semi-real data (real data covariables but synthetic regression coefficients and noise). Furthermore, we present some theoretical bounds for the bias in subsequent least squares estimation, using the selected variables from the first stage, which have direct implications for construction of p-values for regression coefficients.     

A Multi-criteria Decision Support System for R&D Project Selection

This paper describes the development of a decision support system for the selection of a portfolio of R&D projects, which was carried out for a large electricity utility corporation. The DSS was constructed round a reference point approach for the underlying multi-criteria decision problem. The application of this approach did require a less usual form of scalarizing function as well as a heuristic algorithm for solving a non-linear knapsack problem. Practical aspects of the implementation of the multi-criteria approach in a DSS operating on a PC are also discussed.     

A mixed R&D projects and securities portfolio selection model

The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper, we propose a mixed single-stage R&D projects and multi-stage securities portfolio selection model. Specifically, we present a bi-objective mixed-integer stochastic programming model. Moreover, we use semi-absolute deviation risk functions to measure the risk of mixed asset portfolio. Based on the idea of moments approximation method via linear programming, we propose a scenario generation approach for the mixed single-stage R&D projects and multi-stage securities portfolio selection problem. The bi-objective mixed-integer stochastic programming problem can be solved by transforming it into a single objective mixed-integer stochastic programming problem. A numerical example is given to illustrate the behavior of the proposed mixed single stage R&D projects and multi-stage securities portfolio selection model.     

Optimal decision indices for R&D project evaluation in the pharmaceutical industry: Pearson index versus Gittins index

This paper examines issues related to various decision-based analytic approaches to sequential choice of projects, with special motivation from and application in the pharmaceutical industry. In particular, the Pearson index and Gittins index are considered as key strategic decision-making tools for the selection of R&D projects. It presents a proof of optimality of the Pearson index based on the Neyman–Pearson lemma. Emphasis is also given to how a project manager may differentiate between the two indices based on concepts from statistical decision theory. This work demonstrates and justifies the correct use of the Pearson index.     

R&D Project Selection in a Multidimensional Environment: A Practical Approach

This paper investigates the R&D project selection problem within government departments. The Department of National Defence is taken as a case in point. The multidimensional character of the problem is detailed, and existing methods for priority allocation are briefly surveyed. Two basic models for selecting projects are described. One of these, the ordinal intersection method, was found to be the most appropriate for the actual problem settings investigated. An illustrative example is given, and concluding remarks are made.     

Selection among ranked projects under segmentation,policy and logical constraints

The paper proposes a method for project selection under a specific decision situation, where a final selection is guided by two aspects: (1) satisfaction of certain segmentation, policy and/or logical constraints, and (2) assurance that the individual evaluation of the projects is respected to the maximum degree. This approach is somewhat different than the usual portfolio optimization, where combinations of projects are compared without special concern on respecting the project’s ranking. The entire process is implemented in two phases: the projects are first ranked, usually through a multicriteria approach. The obtained complete preorder of the projects is then used in an integer programming module in order to effectively drive the final selection that satisfies the segmentation and/or logical constraints. The innovative part of the proposed approach is the way it overcomes the well-known bias towards low cost projects which is caused by the knapsack formulation commonly used in the integer programming phase. Actually this is the main source of divergence between the final selection and the initial complete preorder of the projects. The proposed method improves an agreement between the final selection of projects obtained from the integer programming model and the ranking obtained from the multicriteria approach.     

Social Motives and Expectations in One-Shot Asymmetric Prisoner's Dilemmas

We propose a formal-behavioral framework with 3 components: nonselfish motives, expectations about others' nonselfish motives, and a game-theoretic component. For nonselfish motives, 3 nonstandard utility models representing altruism, inequality aversion, and norms are considered. Expectations are modeled as certain versus uncertain expectations. The game-theoretic component predicts behavior of actors and actors' expectations about behaviors of others. This framework is applied to asymmetric one-shot prisoner's dilemmas; predictions are tested experimentally. Formal analyses show that asymmetry provides new predictions through which nonstandard utility-expectation models can be distinguished. Empirical tests show that the inequality aversion model does considerably worse than altruistic and normative variants. Statistical tests for own motives, expected motives, and the association between the two are provided, while accounting for decision noise.     

Object technology software selection: a case study

The goal of this paper is to present a case study focusing on object technology assessment in a computer service industry. We develop decision models to give proper recommendations for object-oriented software projects. The assessment uses a quantitative approach, in which a mixed-integer linear programming model and a multi-objective model were formulated and applied. By reducing the element of subjectivity, these formal models led to consistent tool selection. By separating the data and models, the models can be reused in subsequent software development projects. Finally, by allowing users to specify their objectives and requirements and by providing a sensitivity analysis of the results, this approach also increases customer orientation.     

A zero-one model for project portfolio selection and scheduling

A zero-one integer linear programming model is proposed for selecting and scheduling an optimal project portfolio, based on the organisation's objectives and constraints such as resource limitations and interdependence among projects. The model handles some of the issues that frequently arise in real world applications but are not addressed by previously suggested models, such as situations in which the amount of available and consumed resources varies in different periods. It also allows for interactive adjustment following the optimisation process, to provide decision makers a method for controlling portfolio selection, based on criteria that may be difficult to elicit directly. It is critical for such a system to provide fast evaluation of alternatives the decision makers may want to examine, and this requirement is addressed. The proposed model not only suggests projects that should be incorporated in the optimal portfolio, but it also determines the starting period for each project. Scheduling considerations can have a major impact on the combination of projects that can be incorporated in the portfolio, and may allow the addition of certain projects to the portfolio that could not have been selected otherwise. An example problem is described and solved with the proposed model, and some areas for future research are discussed.     

Multiple criteria linear programming model for portfolio selection

The portfolio selection problem is usually considered as a bicriteria optimization problem where a reasonable trade-off between expected rate of return and risk is sought. In the classical Markowitz model the risk is measured with variance, thus generating a quadratic programming model. The Markowitz model is frequently criticized as not consistent with axiomatic models of preferences for choice under risk. Models consistent with the preference axioms are based on the relation of stochastic dominance or on expected utility theory. The former is quite easy to implement for pairwise comparisons of given portfolios whereas it does not offer any computational tool to analyze the portfolio selection problem. The latter, when used for the portfolio selection problem, is restrictive in modeling preferences of investors. In this paper, a multiple criteria linear programming model of the portfolio selection problem is developed. The model is based on the preference axioms for choice under risk. Nevertheless, it allows one to employ the standard multiple criteria procedures to analyze the portfolio selection problem. It is shown that the classical mean-risk approaches resulting in linear programming models correspond to specific solution techniques applied to our multiple criteria model. This revised version was published online in June 2006 with corrections to the Cover Date.     

动态主动打断项目组合选择问题的模型研究

动态主动打断项目组合选择问题是项目组合选择研究的新方向,然而该问题中涉及的六个重要参数都是不确定的,需要通过预测或估算才能得到,导致项目组合选择结果存在风险,最优解随着参数的变动发生变化。针对这种情况,本文首先提出了一个适合敏感性分析的动态主动打断项目组合选择新模型,并将其线性化;其次,运用GAMS＼BARON求解算例,对比动态和静态主动打断项目组合选择模型的结果,验证了动态线性模型的优越性;最后,分别求解六个不确定性参数的敏感性系数,并进行了局部敏感性分析。结果表明:第一,动态主动打断项目组合选择线性模型既可以统筹安排新、旧项目,又能增加企业收益;第二,根据系数敏感性的排名,企业应当有区别地对待六个不确定性参数;第三,新项目投资和项目收益的敏感性大小和排序,会随着参数变动而变化。     

HARA frontiers of optimal portfolios in stochastic markets

In this paper, we consider the optimal portfolio selection problem in continuous-time settings where the investor maximizes the expected utility of the terminal wealth in a stochastic market. The utility function has the structure of the HARA family and the market states change according to a Markov process. The states of the market describe the prevailing economic, financial, social and other conditions that affect the deterministic and probabilistic parameters of the model. This includes the distributions of the random asset returns as well as the utility function. We analyzed Black–Scholes type continuous-time models where the market parameters are driven by Markov processes. The Markov process that affects the state of the market is independent of the underlying Brownian motion that drives the stock prices. The problem of maximizing the expected utility of the terminal wealth is investigated and solved by stochastic optimal control methods for exponential, logarithmic and power utility functions. We found explicit solutions for optimal policy and the associated value functions. We also constructed the optimal wealth process explicitly and discussed some of its properties. In particular, it is shown that the optimal policy provides linear frontiers.     

Objective comparisons of the optimal portfolios corresponding to different utility functions

This paper considers the effects of some frequently used utility functions in portfolio selection by comparing the optimal investment outcomes corresponding to these utility functions. Assets are assumed to form a complete market of the Black–Scholes type. Under consideration are four frequently used utility functions: the power, logarithm, exponential and quadratic utility functions. To make objective comparisons, the optimal terminal wealths are derived by integration representation. The optimal strategies which yield optimal values are obtained by the integration representation of a Brownian martingale. The explicit strategy for the quadratic utility function is new. The strategies for other utility functions such as the power and the logarithm utility functions obtained this way coincide with known results obtained from Merton’s dynamic programming approach.